Deep Learning in High-Resolution Anoscopy: Assessing the Impact of Staining and Therapeutic Manipulation on Automated Detection of Anal Cancer Precursors

Abstract

Introduction: High-resolution anoscopy (HRA) is the gold standard for detecting anal squamous cell cancer (ASCC) precursors. Preliminary studies on the application of artificial intelligence (AI) models to this modality have revealed promising results. However, the impact of staining techniques and anal manipulation on the effectiveness of these algorithms has not been evaluated. We aimed to develop a deep learning system for automatic differentiation of high (HSIL) versus low-grade (LSIL) squamous intraepithelial lesions in HRA images in different subsets of patients (non-stained, acetic acid, lugol, and after manipulation). Methods: A convolutional neural network (CNN) was developed to detect and differentiate high and low-grade anal squamous intraepithelial lesions based on 27,770 images from 103 HRA exams performed in 88 patients. Subanalyses were performed to evaluate the algorithm’s performance in subsets of images without staining, acetic acid, lugol, and after manipulation of the anal canal. The sensitivity, specificity, accuracy, positive and negative predictive values, and area under the curve (AUC) were calculated. Results: The CNN achieved an overall accuracy of 98.3%. The algorithm had a sensitivity and specificity of 97.4% and 99.2%, respectively. The accuracy of the algorithm for differentiating HSIL vs LSIL varied between 91.5% (post-manipulation) and 100% (lugol) for the categories at subanalysis. The AUC ranged between 0.95 and 1.00. Discussion: The introduction of AI to HRA may provide an accurate detection and differentiation of ASCC precursors. Our algorithm showed excellent performance at different staining settings. This is extremely important as real-time AI models during HRA exams can help guide local treatment or detect relapsing disease.